House Thermal Model Estimation: Robustness Across Seasons and Setpoints

TL;DR

This paper compares three optimization algorithms for estimating house thermal models, demonstrating their robustness across different seasons and temperature setpoints to support demand response strategies.

Contribution

It introduces a systematic evaluation of RC thermal model parameter estimation methods for robustness across seasonal and setpoint variations.

Findings

MLE provides the most robust models across seasons.

NLS and BE are computationally efficient but less robust.

Model selection depends on desired robustness and computational constraints.

Abstract

Achieving the flexibility from house heating, cooling, and ventilation systems (HVAC) has the potential to enable large-scale demand response by aggregating HVAC load adjustments across many homes. This demand response strategy helps distribution grid to flexibly ramp-up or ramp-down local load demand so that it can optimally match the bulk power system generation profile. However, achieving this capability requires house thermal models that are both computationally efficient and robust to operating conditions. In this work, parameters of the Resistance-Capacitance (RC) network thermal model for houses are estimated using three optimization algorithms: Nonlinear Least Squares (NLS), Batch Estimation (BE), and Maximum Likelihood Estimation (MLE). The resulting models are evaluated through a Forward-Simulation across four different seasons and three setpoints. The results illustrate a principled way of selecting reduced order models and estimation methods with respect to the robustness offered to seasonal and setpoint variations in training-testing datasets